DETECTION OF ADULT PEACH FRUIT MOTH BASED ON IMPROVED YOLOv8m

The peach fruit moth was a fruit-eating pest and one of the major pests of fruit trees in China, Korea, Japan, and Australia. Due to long-term problems such as improper control methods, low technical quality, and untimely treatment, the yield and efficiency of fruit products were greatly affected, which constrained the development of the fruit industry. This paper developed a method for detecting adult peach fruit moths based on an improved YOLOv8m to address the challenging problem of manually detecting peach fruit moths. To increase the Receptive Field of the model, v7Down Sampling was introduced in its backbone network. Then, the channel-prioritized Convolutional Attention Mechanism Module (CPCA), which dynamically allocated the spatial attention weights on each channel, reducing the noise and the algorithm’s complexity, was incorporated. Finally, the inner-WIoU loss function was introduced to enhance the convergence and generalization of the bounding box. The precision (P) of the improved model increased by 3.4 percentage points compared to YOLOv8m. The recall (R) improved by 2.1 percentage points, and the mAP improved by 1.2 percentage points. The single-category precision (AP) for peach fruit moth detection improved by 2.4 percentage points. Moreover, the weight size, number of model parameters, and computational volume were reduced by 3.6MB, 1.8M, and 1.7G, respectively. This achieved an improvement in the model's effectiveness in detecting adult peach fruit moths without increasing the model's complexity. The results provided strong technical support for the subsequent real-time monitoring of the peach fruit moth.